A Moving-Knife Solution to the Four-Person Envy-Free Cake-Division Problem

Envy-freeness

Bending the Medicare Cost Curve in 12 Months or Less: How Preventative Health Care can Yield Significant Near-Term Savings for Acute Care in Alberta

Over the course of more than 30 years, a series of Canadian government commissions and health policy researchers have repeatedly identified the importance of “bending the cost curve” to sustain publicly funded health care, and the potential to do so through upstream investment in health promotion and disease prevention. So far, however, the level of public investment in prevention represents only a slight portion of total public health care expenditure, largely consisting of traditional public health initiatives such as vaccinations, disease screening and information campaigns. This study of the Pure North S’Energy Foundation’s preventative health care program — wherein health care usage by program participants was measured against age- and sex-matched control samples — finds that the sort of preventative health care services offered by Pure North can lead to genuine and significant near-term cost savings for Canada’s single-payer health care system. Participants in the first year of the program required 25 per cent fewer hospital visits and 17 per cent fewer emergency room visits compared to the control group. Among those who persisted in the program for a year or longer, the effects were even more significant: 45 per cent fewer hospital visits in the year after joining, and 28 per cent fewer visits to emergency departments, compared to the control group. This represents real cost savings for a public health service: From $388 per person who joined the program to$677 per person who persisted beyond the first year. As a proportion of annual health spending for these participants on hospitals, emergency departments and general practitioners, this represents a cost reduction ranging from 22 to 39 per cent. If the Alberta government were able to implement this kind of program provincewide (at an estimated cost of $500 per participant), and were to realize similar results in terms of reduced strain on acute care services, it is possible that the province could free-up the equivalent of 1,632 hospital beds every year. That is roughly the same as building two entirely new hospitals each on the scale of Calgary’s Foothills Medical Centre. This demonstrates that “bending the cost curve” for public health care spending is not merely something that is realizable in the long term, but rather in the immediate future, as quickly as within a year after this kind of program could be implemented province-wide. And yet, the near-term savings in acute care services represent only the first wave of benefits. The prevalence of chronic diseases and conditions, including diabetes, heart disease, cancer and mental illness, have been rising and are projected to keep doing so over the coming decade. The Pure North program aims to prevent and address these health conditions and chronic diseases through a combination of screening and testing, lifestyle modification, nutrition education, the identification of nutritional deficiencies, and dietary supplements. The long-term benefits of a Pure North-style program implemented province-wide in Alberta are likely to be that much greater as the prevalence of diabetes, heart disease, cancer and mental illness is tempered through the use of widespread preventative care. Then there are the broader “indirect benefits” of a generally healthier population: higher labour productivity, higher incomes and greater well-being. These returns to the Alberta government, and taxpayer, have the potential to be as large, if not larger, than the direct benefits of significantly reduced acute care costs

Laying the Foundation for Policy: Measuring Local Prevalence for Autism Spectrum Disorder

Claims have been made that families with children living with autism spectrum disorders (ASD) have been migrating to Alberta because of higher funding available for ASD supports compared to other provinces. The legitimacy of these claims, along with many others about the adequacy or inadequacy of funding for supporting persons living with ASD, has not been evaluated because we simply don’t know how many people in Alberta are living with ASD. Typically in Canada, ASD prevalence is reported in national figures, based on international estimates. Canadian prevalence estimates for ASD are needed. With no national surveillance system in place, national estimates are difficult to determine. In addition, such broad measurements are problematic as they may not adequately inform the service delivery needs for specific jurisdictions. A new study shows that 1,711, or 1 in 94, school age children in the Calgary region have an ASD diagnosis. As this number matches what is often reported for the national prevalence of ASD, it suggests that Alberta’s relatively higher ASD funding is not inducing in-migration of families seeking better support. The data also show that the prevalence is higher in elementary-grade children, with a diagnosis in one of every 86 children. In the senior grades, there are significantly fewer students with ASD diagnoses, specifically within the Calgary Board of Education. There is no evident reason for diagnoses to seemingly dematerialize in the older grades. These students could be dropping out or choosing home-schooling in greater numbers. Possibly there has been an increase in prevalence. These prevalence estimates help to inform the demand for special-needs services within the local school system. In addition, there is growing concern that upon graduation there is a “support cliff” resulting from a less systematized, less generous support system available for adults with neurodevelopmental disability. Families that need support for ASD face enough challenges; it is critical for policy-makers to be aware of the extent of the situation in their own jurisdiction so as to develop the right kinds of supports for these families

Auditory-inspired morphological processing of speech spectrograms: applications in automatic speech recognition and speech enhancement

New auditory-inspired speech processing methods are presented in this paper, combining spectral subtraction and two-dimensional non-linear filtering techniques originally conceived for image processing purposes. In particular, mathematical morphology operations, like erosion and dilation, are applied to noisy speech spectrograms using specifically designed structuring elements inspired in the masking properties of the human auditory system. This is effectively complemented with a pre-processing stage including the conventional spectral subtraction procedure and auditory filterbanks. These methods were tested in both speech enhancement and automatic speech recognition tasks. For the first, time-frequency anisotropic structuring elements over grey-scale spectrograms were found to provide a better perceptual quality than isotropic ones, revealing themselves as more appropriate—under a number of perceptual quality estimation measures and several signal-to-noise ratios on the Aurora database—for retaining the structure of speech while removing background noise. For the second, the combination of Spectral Subtraction and auditory-inspired Morphological Filtering was found to improve recognition rates in a noise-contaminated version of the Isolet database.This work has been partially supported by the Spanish Ministry of Science and Innovation CICYT Project No. TEC2008-06382/TEC.Publicad

Old and NewMoving-Knife Schemes

resource allocation ; decision making ; economic theory

How Should Voting on Related Propositions be Conducted?

Voting

Supervised Domain Adaptation using Graph Embedding

Getting deep convolutional neural networks to perform well requires a large amount of training data. When the available labelled data is small, it is often beneficial to use transfer learning to leverage a related larger dataset (source) in order to improve the performance on the small dataset (target). Among the transfer learning approaches, domain adaptation methods assume that distributions between the two domains are shifted and attempt to realign them. In this paper, we consider the domain adaptation problem from the perspective of dimensionality reduction and propose a generic framework based on graph embedding. Instead of solving the generalised eigenvalue problem, we formulate the graph-preserving criterion as a loss in the neural network and learn a domain-invariant feature transformation in an end-to-end fashion. We show that the proposed approach leads to a powerful Domain Adaptation framework; a simple LDA-inspired instantiation of the framework leads to state-of-the-art performance on two of the most widely used Domain Adaptation benchmarks, Office31 and MNIST to USPS datasets.Comment: 7 pages, 3 figures, 3 table